We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of a cointegrating relation among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomena among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data, is less accurate than the prediction based on micro equations, and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading.

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.

End-Use Disaggregation for Small Business Market Outlook

According to our latest research, the global end-use disaggregation for small business market size reached USD 1.52 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.4% expected through 2033. This growth trajectory will propel the market to a forecasted value of USD 4.71 billion by 2033. The primary growth factor driving this market is the increasing adoption of advanced energy analytics and smart metering technologies by small businesses seeking operational efficiency and cost reduction.

The growth of the end-use disaggregation for small business market is fueled by the rising demand for granular energy consumption data. Small businesses are increasingly aware of the need to optimize energy usage not only to reduce operational costs but also to meet stringent sustainability goals. As energy prices continue to fluctuate globally, the ability to monitor and control consumption at the device or process level provides a significant advantage. Non-intrusive load monitoring and submetering technologies are becoming essential tools for small businesses, enabling them to gain actionable insights without the need for large-scale infrastructure changes. This increased focus on energy management is further amplified by regulatory pressures and incentives that encourage efficient energy use.

Another key growth factor is the proliferation of smart meters and advanced analytics platforms. The integration of these technologies allows small businesses to access real-time data, identify inefficiencies, and implement targeted interventions to reduce waste. The evolution of cloud-based solutions makes deployment more accessible and scalable for small enterprises, eliminating the need for substantial upfront investments in IT infrastructure. Moreover, the growing ecosystem of third-party service providers offering tailored analytics and reporting solutions is expanding the market reach, especially in regions with high energy costs and supportive regulatory frameworks.

The increasing emphasis on sustainability reporting and demand response programs is also propelling market growth. Small businesses are now expected to demonstrate their commitment to environmental stewardship, and detailed energy consumption data is crucial for accurate reporting and compliance. End-use disaggregation solutions empower businesses to participate in demand response initiatives, allowing them to adjust usage during peak periods and benefit from utility incentives. This not only enhances their bottom line but also contributes to grid stability and broader environmental goals. As digital transformation accelerates across industries, the adoption of these advanced energy management tools is anticipated to become a standard practice among small businesses globally.

Regionally, North America leads the end-use disaggregation for small business market, supported by high technology adoption rates and favorable government policies. Europe follows closely, driven by stringent energy efficiency regulations and sustainability mandates. The Asia Pacific region is emerging as a high-growth market due to rapid urbanization, increasing energy demand, and a growing focus on smart infrastructure. Latin America and the Middle East & Africa are also witnessing gradual adoption, primarily in urban centers and sectors with high energy intensity. The global market is characterized by a mix of established players and innovative startups, fostering a competitive and dynamic landscape.

Technology Analysis

The technology segment of the end-use disaggregation for small business market is pivotal, encompassing non-intrusive load monitoring (NILM), submetering, smart meters, and advanced analytics. Non-intrusive load monitoring technologies are gaining significant traction due to their ability to analyze aggregate energy data and disaggregate it into individual appliance or system usage without the need for extensive hardware installations. This approach is particularly attract

This paper uses disaggregate inflation data spanning all of consumption to examine: (i) the persistence of disaggregate inflation relative to aggregate inflation; (ii) the distribution of persistence across consumption sectors; and (iii) whether persistence has changed. Assuming mean inflation to be unchanged, disaggregate persistence inflation is consistently below aggregate persistence. Taking into account an early 1990s shift in mean inflation identified by break tests yields much lower estimates of both aggregate and disaggregate persistence for 1984-2002. But with the mean break, average disaggregate persistence is actually as great as aggregate inflation persistence. A factor model provides a natural framework for interpreting the relationship between aggregate and disaggregate persistence.

Currently published data series on the United States household debt service ratio are constructed from aggregate household debt data provided by lenders and estimates of the average interest rate and loan terms of a range of credit products. The approach used to calculate those debt service ratios could be prone to missing changes in loan terms. Better measurement of this important indicator of financial health can help policymakers anticipate and react to crises in household finance. We develop and estimate debt service ratio measures based on individual-level debt payments data obtained from credit bureau data and published estimates of disposable personal income. Our results suggest that aggregate debt service ratios may have understated the payment requirements of households. To the extent possible with two very distinct data sources we examine the details on the composition of household debt service and identify some areas where required payments appear to have varied substantially from the assumptions used in the Board of Governors' aggregate calculation. We then use our technique to calculate both national and state-level debt ratios and break these debt service ratios into debt categories at the national, state level, and metro level. This approach should allow detailed forecasts of debt service ratios based on anticipated changes to interest rates and incomes, which could serve to evaluate the ability of households to cope with potential economic shocks. The ability to disaggregate these estimates into geographic regions or age groups could help to identify the severity of the effects on more exposed groups.

Major differences from previous work: For level 2 catch: Catches in tons, raised to match nominal values, now consider the geographic area of the nominal data for improved accuracy. Captures in "Number of fish" are converted to weight based on nominal data. The conversion factors used in the previous version are no longer used, as they did not adequately represent the diversity of captures. Number of fish without corresponding data in nominal are not removed as they were before, creating a huge difference for this measurement_unit between the two datasets. Nominal data from WCPFC includes fishing fleet information, and georeferenced data has been raised based on this instead of solely on the triplet year/gear/species, to avoid random reallocations. Strata for which catches in tons are raised to match nominal data have had their numbers removed. Raising only applies to complete years to avoid overrepresenting specific months, particularly in the early years of georeferenced reporting. Strata where georeferenced data exceed nominal data have not been adjusted downward, as it is unclear if these discrepancies arise from missing nominal data or different aggregation methods in both datasets. The data is not aggregated to 5-degree squares and thus remains unharmonized spatially. Aggregation can be performed using CWP codes for geographic identifiers. For example, an R function is available: source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/transform_cwp_code_from_1deg_to_5deg.R") Level 0 dataset has been modified creating differences in this new version notably : The species retained are different; only 32 major species are kept. Mappings have been somewhat modified based on new standards implemented by FIRMS. New rules have been applied for overlapping areas. Data is only displayed in 1 degrees square area and 5 degrees square areas. The data is enriched with "Species group", "Gear labels" using the fdiwg standards. These main differences are recapped in the Differences_v2018_v2024.zip Recommendations: To avoid converting data from number using nominal stratas, we recommend the use of conversion factors which could be provided by tRFMOs. In some strata, nominal data appears higher than georeferenced data, as observed during level 2 processing. These discrepancies may result from errors or differences in aggregation methods. Further analysis will examine these differences in detail to refine treatments accordingly. A summary of differences by tRFMOs, based on the number of strata, is included in the appendix. Some nominal data have no equivalent in georeferenced data and therefore cannot be disaggregated. What could be done is to check for each nominal data without equivalence if a georeferenced data exists in different buffers, and to average the distribution of this footprint. Then, disaggregate the nominal data based on the georeferenced data. This would lead to the creation of data (approximately 3%), and would necessitate reducing/removing all georeferenced data without a nominal equivalent or with a lesser equivalent. Tests are currently being conducted with and without this. It would help improve the biomass captured footprint but could lead to unexpected discrepancies with current datasets. For level 0 effort : In some datasets—namely those from ICCAT and the purse seine (PS) data from WCPFC— same effort data has been reported multiple times by using different units which have been kept as is, since no official mapping allows conversion between these units. As a result, users have be remind that some ICCAT and WCPFC effort data are deliberately duplicated : in the case of ICCAT data, lines with identical strata but different effort units are duplicates reporting the same fishing activity with different measurement units. It is indeed not possible to infer strict equivalence between units, as some contain information about others (e.g., Hours.FAD and Hours.FSC may inform Hours.STD). in the case of WCPFC data, effort records were also kept in all originally reported units. Here, duplicates do not necessarily share the same “fishing_mode”, as SETS for purse seiners are reported with an explicit association to fishing_mode, while DAYS are not. This distinction allows SETS records to be separated by fishing mode, whereas DAYS records remain aggregated. Some limited harmonization—particularly between units such as NET-days and Nets—has not been implemented in the current version of the dataset, but may be considered in future releases if a consistent relationship can be established.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has been clipped to the Gloucester PAE.

Dataset History

1.\tJoe Bell (GA) has clipped the surface water licences to the Gloucester PAE. This clip contains the works associated with the sharecomponent/ entitlement. This can be a many to many relationship. . A work is a surface water extraction point.

SW_licences_GloucesterPAE_Clip.dbf

2.\tShare component/ entitlement information was stored in the SW_Gloucester_COMBINED_v4.csv worksheet

3.\tTotal volume of share component/ entitlement is 10,786ML

4.\tThe works and share/component information was joined using Access, linking the CWlicence to the WAorCA_link. This links the volumetric entitlement to the works location.

5.\tThis link also created share components that had a 0 entitlement which are licences that have been converted to unbundled licences in the new Water Act

6.\tBy filtering out the 0 entitlement, the number of works linked to a share/component or entitlement with a specified volume was 212 with a total of 10,786ML. Worksheet FilteredIndividualSWLicences

7.\tWhere there was more than one works per licence, an additional column was add COUNT_CWLICENSE. This shows where the share component/ entitlement is double counted as it is matched to each work with the full allocation.

8.\tAn additional column was added SHARE_PER_WORKS which divides the share component/ entitlement by the number of works to give an allocation per works.

9.\tThe SHARE_PER_WORKS column allows you to plot the works with the share component in ArcGIS without double counting the allocation.

1. A glossary of terms used ini the water licensing is included here: http://registers.water.nsw.gov.au/wma/Glossary.jsp

11.\tAn additional worksheet was added to aggregate the data into Water Sources and Management Zones. The Water Sources and Management Zones were provided by NSW Office of Water

CombinedWSP_WSOURCES_31July2013.gdb\Geographic_GDA94\WSP_COMBINED_31July2013

12.\tThe Avon River does not have management zones. Therefore data can only be viewed for the water source.

13.\tAll other works can be aggregated to the Water Source, or the management zone depending on how you want to aggregate or disaggregate the data.

relevant fields:

CWLICENSE: works licence number

COUNT_CWLICENSE: Where there was more than one works per licence

SHARE_PER_WORKS: Share component divided by number of works to ensure no double counting

STATUS_DES: Status description as active, current, cancelled

LICENCE_iS: licensed issued date

LICENCE_LO: licence lodged date

LICENCE_P: Licence purpose eg. stock and domestic, town supply, irrigation

WORK_TYPE: pump, excavation etc

WORK_TYPE_: diversion or storage

MAJOR_CATC: major surface water catchement

NAME_OF_TH: water sharing plan the licence belongs to

WATER_SHAR: water sharing plan the licence belongs to

WATER_SOUR: water source

MANAGEMENT: management zone

WSP_STATUS: Status of the water sharing plan

START_DATE: Start date of the water sharing plan

END_DATE: end date of the water sharing plan

LICENSEorAPPROVAL: licence or approval number

Status: Cancelled or current (or blank)

ShareC: Share component attached to the licence

WAorCAlink:a combined water supply works / water use approval

LINKED_TO_AL:This is the identification number for an access licence which is shown on the licence certificate or on a search printout of the licence obtained from the access licence register run by Land and Property Information.

Dataset Citation

Bioregional Assessment Programme (XXXX) NSW Office of Water SW licences - Gloucester PAE v2 21022014. Bioregional Assessment Derived Dataset. Viewed 14 June 2016, http://data.bioregionalassessments.gov.au/dataset/f0a75a2b-233f-40a4-82cb-1929f2bee8c6.

Dataset Ancestors

• Derived From Subcatchment boundaries within and nearby the Gloucester subregion

• Derived From Bioregional Assessment areas v02

• Derived From Australian Coal Basins

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012.

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01

• Derived From Bioregional Assessment areas v01

• Derived From Geofabric Hydrology Reporting Catchments - V2.1

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From NSW Office of Water SurfaceWater licences in the Gloucester PAE

• Derived From GEODATA TOPO 250K Series 3

• Derived From Australian Geological Provinces, v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Gloucester Coal Basin

• Derived From Geological Provinces - Full Extent

• Derived From GLO Preliminary Assessment Extent

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

Under many aqueous conditions, metal oxide nanoparticles attract other nanoparticles and grow into fractal aggregates as the result of a balance between electrostatic and Van Der Waals interactions. Although particle coagulation has been studied for over a century, the effect of light on the state of aggregation is not well understood. Since nanoparticle mobility and toxicity have been shown to be a function of aggregate size, and generally increase as size decreases, photo-induced disaggregation may have significant effects. We show that ambient light and other light sources can partially disaggregate nanoparticles from the aggregates and increase the dermal transport of nanoparticles, such that small nanoparticle clusters can readily diffuse into and through the dermal profile, likely via the interstitial spaces. The discovery of photoinduced disaggregation presents a new phenomenon that has not been previously reported or considered in coagulation theory or transdermal toxicological paradigms. Our results show that after just a few minutes of light, the hydrodynamic diameter of TiO2 aggregates is reduced from ∼280 nm to ∼230 nm. We exposed pigskin to the nanoparticle suspension and found 200 mg kg−1 of TiO2 for skin that was exposed to nanoparticles in the presence of natural sunlight and only 75 mg kg−1 for skin exposed to dark conditions, indicating the influence of light on NP penetration. These results suggest that photoinduced disaggregation may have important health implications.

Gold nanoparticles linked to linear carboxylated dextran chains were attached to 3-aminopropyltriethoxysilane-functionalized glass surfaces. This method provides novel hybrid nanostructures on a surface with the unique optical properties of gold nanoparticles. The particles attached to the surface retain the capability to aggregate and disaggregate in response to their environment. This procedure presents an alternative method to the immobilization of gold nanoparticles onto planar substrates. Compared to gold nanoparticle monolayers, larger particle surface densities were obtained. Exposure to hydrophobic environments changes the conformation of the hydrophilic dextran chains, causing the gold nanoparticles to aggregate and inducing changes in the absorption spectrum such as red-shifting and broadening of the plasmon absorption peaks. These changes, characteristic of particle aggregation, are reversible. When the substrates are dried and then immersed in an aqueous environment, these changes can be visually observed in a reversible fashion and the sample changes color from the red color of colloidal gold to a bluish-purple color of aggregated nanoparticles. Surface-bound nanoparticles that retain their mobility when attached to a surface by means of a flexible polymer chain could expand the use of aggregation-based assays to solid substrates.

Recent studies argue that cross-country labor productivity differences are much larger in agriculture than in the aggregate. We reexamine the agricultural productivity data underlying this conclusion using new evidence from disaggregate sources. We find that for the world's staple grains—maize, rice, and wheat—cross-country differences in the quantity of grain produced per worker are enormous according to both micro- and macrosources. Our findings validate the idea that understanding agricultural productivity is at the heart of understanding world income inequality.

Protein-Protein, Genetic, and Chemical Interactions for Harari A (2022):Hsp104 N-terminal domain interaction with substrates plays a regulatory role in protein disaggregation. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Heat shock protein 104 (Hsp104) protein disaggregases are powerful molecular machines that harness the energy derived from ATP binding and hydrolysis to disaggregate a wide range of protein aggregates and amyloids, as well as to assist in yeast prion propagation. Little is known, however, about how Hsp104 chaperones recognize such a diversity of substrates, or indeed the contribution of the substrate-binding N-terminal domain (NTD) to Hsp104 function. Herein, we present a NMR spectroscopy study, which structurally characterizes the Hsp104 NTD-substrate interaction. We show that the NTD includes a substrate-binding groove that specifically recognizes exposed hydrophobic stretches in unfolded, misfolded, amyloid and prion substrates of Hsp104. In addition, we find that the NTD itself has chaperoning activities which help to protect the exposed hydrophobic regions of its substrates from further misfolding and aggregation, thereby priming them for threading through the Hsp104 central channel. We further demonstrate that mutations to this substrate-binding groove abolish Hsp104 activation by client proteins and keep the chaperone in a partially inhibited state. The Hsp104 variant with these mutations also exhibited significantly reduced disaggregation activity and cell survival at extreme temperatures. Together, our findings provide both a detailed characterization of the NTD-substrate complex and insight into the functional regulatory role of the NTD in protein disaggregation and yeast thermotolerance.

Protein-Protein, Genetic, and Chemical Interactions for Kirstein J (2017):In vivo properties of the disaggregase function of J-proteins and Hsc70 in Caenorhabditis elegans stress and aging. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Protein aggregation is enhanced upon exposure to various stress conditions and aging, which suggests that the quality control machinery regulating protein homeostasis could exhibit varied capacities in different stages of organismal lifespan. Recently, an efficient metazoan disaggregase activity was identified in vitro, which requires the Hsp70 chaperone and Hsp110 nucleotide exchange factor, together with single or cooperating J-protein co-chaperones of classes A and B. Here, we describe how the orthologous Hsp70s and J-protein of Caenorhabditis elegans work together to resolve protein aggregates both in vivo and in vitro to benefit organismal health. Using an RNAi knockdown approach, we show that class A and B J-proteins cooperate to form an interactive flexible network that relocalizes to protein aggregates upon heat shock and preferentially recruits constitutive Hsc70 to disaggregate heat-induced protein aggregates and polyQ aggregates that form in an age-dependent manner. Cooperation between class A and B J-proteins is also required for organismal health and promotes thermotolerance, maintenance of fecundity, and extended viability after heat stress. This disaggregase function of J-proteins and Hsc70 therefore constitutes a powerful regulatory network that is key to Hsc70-based protein quality control mechanisms in metazoa with a central role in the clearance of aggregates, stress recovery, and organismal fitness in aging.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The difference between NSW Office of Water SW licences - NSSyd v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use. This has not been been clipped to North and South Sydney PAEs. Dataset History The difference between NSW Office of Water SW licences - NSSyd v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. Where purpose = domestic; or domestic & stock; or stock then it was classed as 'basic water right'. Where it is listed as both a domestic/stock and a licensed use such as irrigation, it was classed as a 'water access right.' All other take and use were classed as a 'water access right'. Dataset Citation Bioregional Assessment Programme (2014) NSW Office of Water SW Offtakes Processed - North & South Sydney, v3 12032014. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/7dc3a047-19a2-46ed-b519-b5e4f393aea1. Dataset Ancestors Derived From NSW Office of Water Surface Water Offtakes - North & South Sydney v1 24102013 Derived From NSW Office of Water SW Offtakes Processed - North & South Sydney, v2 07032014 Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The aim of this dataset was to be able to map each groundwater works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has not been clipped to the Gloucester PAE, therefore the number of economic assets/ relevant licences will drastically reduce once this occurs.

The Worksheet titled 2.1 links the individual licence to a works and a volumetric entitlement. For about 50% of sites, this can be linked to a bore which can be found in the NGIS through the HydroID. . This will allow analysis of depths, lithology and hydrostratigraphy where the data exists.

Data can be aggregated based on water source and water management zone.

Dataset History

Instructions

Aim: To get a one to one ratio of licences numbers to bore IDs.

1) NSW Office of Water Groundwater Licence Extract Gloucester- Oct 2013 datasets have mulitple licence instances if there are more than one WA/CA number. This means that there probably one works or permit to the licence. The aim is to only have one licence instance.

2) Using the pivot results, the individual licence numbers were combined into one colume in the 2.1 Linked Licence Number spreadsheet.

3) Using the new column of licence numbers, several vlookups were created to bring in other data. This includes the bore number from 1.1 From_Arc Spreadsheet and the original data from the 1.2 From_Spreadsheet_WALS and 1.3 From_Spreadsheet_OLD spreadsheets. (Please note, columns of the two spreadsheets 1.2 From_Spreadsheet_WALS and 1.3 From_Spreadsheet_OLD spreadsheets overlap. Where they do they are combined. The only ones that dont are the Share/Entitlement/allocation, these mean different things.

4) A hydro ID column was created, this is a code that links this NSW to the NGIS, which is basically a ".1.1" at the end of the bore code.

5) A quality check "3.4 QA Check Arc and Spreadsheet" was to make sure that all the shape file licences are reperesented in the WALs and Old spreadsheets, which they are.

Please Note

The share componant/entitlement/ allocation is per licence AND per bore. Any licence that had more than one bore had no volumetric amount attached so it can be assumed that the licenced has only one bore and the volume is from licence and bore.

Dataset Citation

Bioregional Assessment Programme (2014) NSW Office of Water Groundwater licence extract linked to spatial locations GLOv2 19022014. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/38c9e2c9-02ac-45a8-a188-936f91a58391.

Dataset Ancestors

• Derived From NSW Office of Water Groundwater Entitlements Spatial Locations

• Derived From NSW Office of Water Groundwater Licence Extract Gloucester - Oct 2013